High data rate closed loop mimo scheme combining transmit diversity and data multiplexing

ABSTRACT

Closed loop multiple-antenna wireless communications system with antenna weights determined by maximizing a composite channel signal-to-interference-plus-noise ratio minimum. Multiplexed symbol streams over subsets of antennas enhance throughput.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority from U.S. provisional patentapplication Appl. Nos. 60/331,718, filed Nov. 21, 2001, 60/339,704,filed Dec. 13, 2001, and 60/343,424, filed Dec. 20, 2001. The followingcopending applications disclose related subject matter and have a commonassignee: Appl. Nos. 10/. . . , filed.

BACKGROUND OF THE INVENTION

[0002] The present invention relates to wireless digital communications,and more particularly to space diversity transmission systems andmethods.

[0003] Wireless communication systems include a large variety ofapproaches, such as frequency division multiple access (FDMA), timedivision multiple access (TDMA), code division multiple access (CDMA),and combinations. FDMA uses separate frequency bands for duplexcommunication; whereas, TDMA partitions a single frequency band intotime slots which as allocated to one or the other end of a communicationlink. CDMA uses a spread spectrum approach.

[0004] Spread spectrum wireless communications utilize a radio frequencybandwidth greater than the minimum bandwidth required for thetransmitted data rate, but many users may simultaneously occupy thebandwidth. Each of the users has a pseudo-random code for “spreading”information to encode it and for “despreading” (by correlation) receivedspread spectrum signals and recovery of information. Such multipleaccess typically appears under the name of code division multiple access(CDMA). The pseudo-random code may be an orthogonal (Walsh) code, apseudo-noise (PN) code, a Gold code, or combinations (modulo-2additions) of such codes. After despreading the received signal at thecorrect time instant, the user recovers the corresponding informationwhile other users' interfering signals appear noise-like. For example,the interim standard IS-95 for such CDMA communications employs channelsof 1.25 MHz bandwidth and a pseudo-random code pulse (chip) intervalT_(C) of 0.8138 microsecond with a transmitted symbol (bit) lasting 64chips. The recent 3GPP wideband CDMA (WCDMA) proposal employs a 3.84 MHzbandwidth and the CDMA code length applied to each information symbolmay vary from 4 chips to 256 chips. Indeed, UMTS (universal mobiletelecommunications system) approach UTRA (UMTS terrestrial radio access)provides a spread spectrum cellular air interface with both FDD(frequency division duplex) and TDD (time division duplex) modes ofoperation. UTRA currently employs 10 ms duration frames partitioned into15 time slots with each time slot consisting of 2560 chips (T_(C)=0.26microsecond).

[0005] The air interface leads to multipath reception, so a RAKEreceiver has individual demodulators (fingers) tracking separate pathsand combines the finger results to improve signal-to-noise ratio (SNR).The combining may use a method such as the maximal ratio combining (MRC)in which the individual detected signals in the fingers are synchronizedand weighted according to their signal strengths or SNRs and summed toprovide the decoding. That is, a RAKE receiver typically has a number ofDLL or TDL code tracking loops together with control circuitry forassigning tracking units to the strongest received paths. Also, anantenna array could be used for directionality by phasing the combinedsignals from the antennas.

[0006] Further, UTRA allows for transmit diversity, both open-loop andclosed-loop (receiver feedback). The open-loop transmit diversityincludes both time-switched transmit diversity (TSTD) and space-timeblock-coding-based transmit diversity (STTD). Closed loop techniquesprovide some significant gain over open-loop transmit diversitytechniques by using channel state information (CSI) at the transmitter.For FDD the CSI can be made available at the transmitter via a feedbackchannel; whereas, for TDD the channel can be directly measured at thetransmitter by exploiting the reciprocity (uplink and downlink using thesame channel).

[0007] The current closed-loop transmit diversity transmits only onedata stream via all the transmit antennas, hence achieves the maximumdiversity gain. However, for a given modulation scheme, its peak datarate is limited. Another possible transmission scheme is to transmit thesame number of data streams as the number of transmit antennas. Whileachieving maximum peak data rate (termed multiplexing gain), thediversity gain of such scheme is limited by the number of receiveantennas, especially when the number of receive antennas is the same asthe number of transmit antennas (which is typically the case). Forinstance, when linear detection is used at the receiver, the diversitygain for each stream is Q-P+1, where Q and P are the number of receiveand transmit antennas, respectively. Hence, it is sometimes desirable touse a transmission scheme that combines transmit diversity and datamultiplexing.

SUMMARY OF THE INVENTION

[0008] The present invention provides multiplexed multi-antenna transmitdiversity adapted to a composite channel of physical channel plusequalization and/or interference cancellation.

[0009] This has the advantages including increased performance forwireless communications.

BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings are heuristic for clarity.

[0011]FIGS. 1a-1 b are flow diagrams.

[0012]FIGS. 2a-2 d illustrate transmitters.

[0013]FIGS. 3a-3 c show receivers.

[0014] FIGS. 4-5 present simulation results.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] 1. Overview

[0016] The preferred embodiment methods determine antenna weightings inclosed-loop multi-antenna multiplexed-data-stream systems byincorporating the effect of multipath interference as well as thedetection scheme used at the receiver for closed-loop transmit diversityweight vector selection. An example of such selection criterion is tomaximize minimum signal-to-interference-plus-noise ratio (SINR) of thecomposite channel (physical channel plus equalization/interferencecancellation), where SINR is defined as the ratio between the averagepower of the desired signal component and the average power ofinterference plus noise.

[0017] Preferred embodiment transmissions and reception use such antennaweightings with adaptive updating and may include multi-inputmulti-output (MIMO) systems; see FIGS. 1a-1 b flow diagrams. Thesemethods apply to the various wireless communications approaches (CDMA,TDMA, etc.), and extend to multiplexed data stream versions. For a TDDsystem the transmitter is also a receiver over the same physical channeland thus can directly estimate the channel; whereas, in an FDD systemthe receiver must provide channel state information to the transmitter.

[0018] The determination of antenna weightings derives from optimizationof SINR of the composite channel, and thus depends upon the detectionmethod. The detection can be by any convenient method such as maximumlikelihood, linear zero-forcing, iterative zero-forcing, linear minimummean square error, iterative minimum mean square error, and so forth.

[0019] For a FDD system the receiver must signal the transmitter. Thuswith an FDD CDMA cellular system having mobiles with multiple antennasthe antenna weighting signaling with be both uplink and downlink.

[0020] Preferred embodiment communications systems use preferredembodiment encoding and decoding methods. FIGS. 2a-2 c illustratepreferred embodiment transmitter functional blocks, and FIGS. 3a-3 bshow preferred embodiment receiver functional blocks.

[0021] In preferred embodiment cellular wireless communications systemsbase stations and mobile users could each include one or more digitalsignal processors (DSPs) and/or other programmable devices with storedprograms for performance of the signal processing of the preferredembodiment methods. Alternatively, specialized circuitry could be used.The base stations and mobile users may also contain analog integratedcircuits for amplification of inputs to or outputs from antennas andconversion between analog and digital; and these analog and processorcircuits may be integrated as a system on a chip (SoC). The storedprograms may, for example, be in ROM or flash EEPROM integrated with theprocessor or external. The antennas may be parts of receivers withmultiple finger RAKE detectors for each user's signals. Exemplary DSPcores could be in the TMS320C6××× or TMS320C5×=33 × families from TexasInstruments.

[0022] 2. TDMA-Based Single Stream Preferred Embodiments

[0023] The single-stream preferred embodiments consider transmission ofa single stream of symbols, . . . , s(n), s(n+1), s(n+2), . . . , from Pantennas (P≧2) with antenna weights w₁, w₂, . . . , w_(p) and receptionby Q antennas (Q≧1) with maximal ratio combining (MRC) of multipathsfollowed by various detection methods. Each detection method leads to aspecific method for determination of transmission antenna weightings.

[0024] For comparison purposes, first look at the simple case withnegligible intersymbol interference. Presume that the channel from Ptransmit antennas (FIG. 2d) to Q receiver antennas (FIG. 3c) has at mostL resolvable paths (L-tap delay line channel model) and that the Q×Pchannel matrix H_(j) of attenuations and phase shifts corresponds to thejth delay line tap. With negligible intersymbol interference and amaximal ratio combining (MRC) receiver, the P antenna weightings W₁, W₂,. . . , w_(p) applied to the symbol stream for transmission over the Pantennas are taken to maximize the reception:$w = {{argmax}_{u \in S}u^{H}\left( {\sum\limits_{1 \leq \quad j \leq \quad L}{H_{j}^{H}H_{j}}} \right)u}$

[0025] where S denotes the set of all allowable weighting vectors, and udenotes a P-vector of antenna weightings u₁, u₂, . . . , u_(p) in S. Forexample, S could be the set of P-vectors u with complex components and∥u∥=1; in this case, w equals the eigenvector of the P×P matrix(Σ_(1≦j≦L) H_(j) ^(H)H_(j)) having the maximum eigenvalue. Whereas, S isa finite set of complex P-vectors with unit norm for FDD CDMA.

[0026] In contrast, the first preferred embodiments presume equalizationin the receiver and use channel state information (CSI) for thecomposite channel (physical channel plus equalizer) to determine the Pantenna weightings. FIGS. 2a, 3 a show a transmitter and receiver for asystem with preferred embodiment antenna weighting determinations whichadapt to the channel conditions; the “delay” function in the receiverallows time for the transmitter to adjust to antenna weightings asdetermined by the receiver and signalled to the transmitter. The (q,p)thelement of H_(j) is the channel from the pth transmit antenna to the qthreceive antenna for the jth delay or multipath. Let . . . , s(n),s(n+1), s(n+2), . . . denote the stream of transmitted symbols.

[0027] First, for a TDMA system the received baseband discrete-timesignal (sampled at the symbol rate, extension to sampling at sub-symbolrate is straightforward) is: ${r(n)} = {\begin{bmatrix}{r_{1}(n)} \\{r_{2}(n)} \\\cdots \\{r_{Q}(n)}\end{bmatrix} = {{\sum\limits_{0 \leq \quad j \leq \quad {L - 1}}{H_{j}{{ws}\left( {n - j} \right)}}} + {{noise}(n)}}}$

[0028] where w is the P-vector of weights used at the transmitter andthe L taps are relabeled 0≦j≦L−1 to coincide with the correspondingdelay. (Code-division differs from the foregoing time-division in thatdespreading in code-division allows direct tracking of multipaths andreplacement of the tapped delay line of time-division with a receiverhaving multiple tracking units.)

[0029] Collect a sequence of N received samples to form one detectionwindow: $r = {\begin{bmatrix}{r(0)} \\{r(1)} \\\cdots \\{r\left( {N - 1} \right)}\end{bmatrix} = {{{H\left( {I_{N} \otimes w} \right)}s} + {noise}}}$where $H = \begin{bmatrix}H_{0} & 0 & 0 & \cdots & 0 & 0 & \cdots & 0 \\H_{1} & H_{0} & 0 & \cdots & 0 & 0 & \cdots & 0 \\H_{2} & H_{1} & H_{0} & \cdots & 0 & 0 & \cdots & 0 \\\vdots & \vdots & \vdots & ⋰ & \vdots & \vdots & \quad & \vdots \\H_{L - 1} & H_{L - 2} & H_{L - 3} & \cdots & H_{0} & 0 & \cdots & 0 \\0 & H_{L - 1} & H_{L - 2} & \cdots & H_{1} & H_{0} & \cdots & 0 \\\vdots & \vdots & \vdots & \quad & \quad & \quad & ⋰ & \quad \\0 & 0 & 0 & \cdots & H_{N - L} & H_{N - L - 1} & \cdots & H_{0}\end{bmatrix}$ ${I_{N} \otimes w} = \begin{bmatrix}w & 0 & 0 & \cdots & 0 \\0 & w & 0 & \cdots & 0 \\0 & 0 & w & \cdots & 0 \\\cdots & \quad & \quad & ⋰ & \quad \\0 & 0 & 0 & \cdots & w\end{bmatrix}$

[0030] Thus r is an NQ-vector, H is an NQ×NP block Toeplitz channelmatrix, (I_(N)

W) is an NPXN matrix, and s is the N-vector of transmitted symbols ofthe detection window: s(0), s(1), . . . , s(N-1). N is presumed largerthan L so the lower left N-L triangle of H is all Q×P 0s. Indeed,practical systems may use values such as N=16 or 32 and L=6 Also,presume within a detection window the channel state information (CSI) isconstant (not updated) and thus also the weights w are constant withinthe detection window.

[0031] Application of a matched filter (including maximal ratiocombining of the tap delays) yields the N×1 output y:

y=(I _(N)

w ^(H))H ^(H) r

[0032] More explicitly, (for n≦N−L): $\begin{matrix}{{y(n)} = {{w^{H}H_{0}^{H}{r(n)}} + {w^{H}H_{1}^{H}{r\left( {n + 1} \right)}} + \ldots + {w^{H}H_{L - 1}^{H}{r\left( {n + L - 1} \right)}}}} \\{= {w^{H}\left\{ \left( {\sum\limits_{0 \leq \quad j \leq \quad {L - 1}}{H_{j}^{H}{r\left( {n + j} \right)}}} \right\} \right.}}\end{matrix}$

[0033] Then in terms of the block of transmitted symbols, s, the outputis: $\begin{matrix}{y = {\left( {I_{N} \otimes w^{H}} \right){H^{H}\left\lbrack {{{H\left( {I_{N} \otimes w} \right)}s} + {noise}} \right\rbrack}}} \\{= {{\left( {I_{N} \otimes w^{H}} \right)H^{H}{H\left( {I_{N} \otimes w} \right)}s} + {\left( {I_{N} \otimes w^{H}} \right)H^{H}{noise}}}}\end{matrix}$

[0034] Next, we consider various detection methods. Different types ofdetection methods can be used at the receiver, such as the simplemaximum ratio combining (MRC) receiver above. However, this type ofreceiver is not resistant to multipath interference. Some examples ofinterference-resistant detection method include the optimal maximumlikelihood detection, linear detection (zero forcing or minimum meansquare error), and iterative detection (zero forcing or minimum meansquare error). The details of each detection method are given below.

[0035] The weight vector w is selected based on a criterion that takesinto account the effect of multipath interference. There are a number ofpossible criteria that can be used, including the Rake-based criterionmentioned in Section 1 (which does not fully account for the effect ofmultipath interference). An example criterion that includes the effectof multipath interference is to select w such that the off-diagonalelements of matrix (I_(N)

w^(H))H^(H)H(I_(N)

w) are minimized in some sense (e.g. minimize the sum of off-diagonalterms, minimize the off-diagonal term with maximum magnitude). Noticethat this criterion does not depend on the receiver type. Differenttypes of receiver, however, respond differently to multipathinterference. Hence, intuitively, the selection criteria that take intoaccount the receiver type (detection method) result in betterperformance. Such receiver-specific selection criteria will be discussedin the following paragraphs.

[0036] In general, the optimal maximum likelihood detection wouldestimate the transmitted symbols s by S which is the vector of symbolsthat minimizes the sum of the errors in the received signal on thereceiver antennas. That is, ŝ = argmin_(s)r − H(I_(N) ⊗ w)s²

[0037] where the minimization is taken over the set of possibletransmitted symbol vectors which depends on the symbol mapping. Theweight vector (w) selection at the receiver can be performed based onsymbol error rate (SER) for maximum likelihood detection (which reflectsbit error rate or frame error rate of the system). It can be shown thatan upper bound of SER is (assuming noise variance is unity)${SER} \leq \quad {\sum\limits_{z \in \Delta}{\kappa_{z}{Q\left( \sqrt{{{H\left( {I_{N} \otimes w} \right)}}^{2}/2} \right)}}}$

[0038] where Δ={(u−v): u,v εS, u≠v }, S is the set of all possibletransmitted symbol vectors, κ_(z) is the multiplicity of z in S, andQ(.) is the Gaussian Q-function. This upper bound can be used forselecting w: choose w that minimizes the SER upper bound. But such amaximum likelihood detection becomes computationally intensive withlarger antenna systems. Both linear and iterative detectors are based onthe idea of interference suppression/-cancellation. Possible methodsinclude zero forcing (ZF) and minimum mean square error (MMSE). In thefollowing, the linear MMSE (LMMSE) and iterative MMSE (IMMSE) detectorsare explained. A zero-forcing-based detectors (LZF and IZF) can beobtained from MMSE analogs by removing the identity term in the matrixinverse.

[0039] Generally, for linear detection use a linear equalizer whichtransforms the matched filter N-vector window output y into N-vectorstatistic z=Fy which will estimate transmitted N-vector of symbols s.The N×N matrix F determines the SINR(n) for symbol s(n) in the window,and the minimum SINR(n) determines the overall system error rate (eitherBER or FER). Consequently, the preferred embodiment methods determinethe antenna weightings w to maximize the minimum SINR(n). That is, givenequalizer F, pick w so that

w=arg min _(uεS) min _(1≦n≦N) SINR(n)

[0040] where u denotes a P-vector of antenna weightings in the set ofallowed weightings S. The dependence of SINR(n) on F and antennaweightings for different detectors is as follows.

[0041] For linear zero-forcing (LZF) detection, the N×N equalizer matrixF is found as the inverse of the channel model:

F=[G ^(H) G] ⁻¹

[0042] where the NQ×N antenna-weighted channel time-window matrix G isgiven by:

G=H(I _(N)

w)

[0043] so G^(H)G is N×N Hermitian and invertible (a 0 eigenvaluecorresponds to either 0 antenna weights, which means no transmission, ora 0 channel, which means no reception). And then SINR(n) is given by:

SINR(n)_(LZF) =ρ/[G ^(H) G]⁻¹ _(n,n)

[0044] where ρ is the normalized power per symbol and [G^(H)G]⁻¹ _(n,n)denotes the row n, column n element of the matrix [G^(H)G]⁻¹. Thus theSINRs for the symbols are proportional to the reciprocals of thediagonal elements of the equalizer matrix.

[0045] Similarly for linear minimum mean square error (LMMSE) detectionthe equalizer matrix F is picked so the mean square error (MSE),E[∥Fy−s∥²], is minimized. The (theoretically derived) lineartransformation F is given by:

F=[ρ ⁻¹ I _(N) +G ^(H) G] ⁻¹

[0046] And the resultant SINR(n) is:SIN  (R(n))_(LMNSE) = ρ/[ρ⁻¹I_(N) + G^(H)G]_(n, n)⁻¹ − 1  

[0047] And for these two linear detectors the preferred embodimentantenna weightings w are computed to maximize the minimum compositechannel SINR; namely, $\begin{matrix}{w_{LZF} = {{argmin}_{u \in S}{\min_{1 \leq \quad n \leq \quad N}{1/\left\lbrack {\left( {I_{N} \otimes u^{H}} \right)H^{H}{H\left( {I_{N} \otimes u} \right)}} \right\rbrack_{n,n}^{- 1}}}}} \\{w_{LMMSE} = {{{argmin}_{u \in S}{\min_{1 \leq \quad n\quad \leq \quad N}{1/\left\lbrack {I_{N} + {{\rho \left( {I_{N} \otimes u^{H}} \right)}H^{H}{H\left( {I_{N} \otimes u} \right)}}} \right\rbrack_{n,n}^{- 1}}}} - 1}}\end{matrix}$

[0048] And when the channel coefficients, H, are updated, the antennaweightings, w, can updated for both transmission and reception. Forexample, in a TDMA cellular telephone system the updating may occurevery 0.5-ms.

[0049] For nonlinear detection, such as iterative (decision-feedback)equalizers, more computations are required than for the correspondinglinear detector. The iterative equalizer is implemented in N steps witheach step making a decision on one of the N symbols in the window. Eachstep includes a linear transformation (ZF or MMSE) followed by a harddecision-feedback (across space and time). That is, a resulting linearlytransformed statistic z=Fy is essentially a soft estimate of a componentof s.

[0050] The SINR for iterative equalizers (IZF or IMMSE) can be computedas for the linear equalizers. Of course, the optimization to determinethe antenna weightings w has higher complexity. The IMMSE detector is asequence of N linear MMSE detection stages, where each detection outputsboth a hard and a soft estimate of one of the N symbols in the detectionblock. The hard estimate is used to regenerate the interference from theso-far estimated symbols which is then subtracted from the receivedsignal, and the difference used for the next linear symbol estimation.More explicitly, presume the symbols are to be estimated in numericalorder and let ŝ_(k) denote the hard estimate of the kth symbol s_(k) andlet the N-vector ŝ^((k)) denote the vector with components 1, 2, . . . ,k equal to ŝ₁, ŝ₂, . . . , ŝ_(k), respectively, and with the remainingN−k components all equal to 0. The iteration's nth step will outputŝ^((n)) from an initialization of Ŝ⁽⁰⁾=0. The nth step (nth lineardetector) proceeds as follows:

[0051] (a) Regenerate the interference created by previously-estimatedsymbols s₁, . . . , s_(n−)1 using the channel matrix; that is, form Gŝ^((n−1)). Note that only the first n−1 rows of blocks of G are usedbecause the last N−n+1 components of ŝ^((n−1)) equal 0, so a simplermatrix with rows of blocks n, n+1, . . . N all 0s could be used.

[0052] (b) Subtract the regenerated interference of substep (a) from thereceived signal to have an interference-cancelled signal: r−G ŝ^((n−1)).

[0053] (c) Apply the linear equalizer filter F to the matched-filtered(N×NQ matrix G^(H)) interference-cancelled signal from substep (b) togenerate a soft output z^((n)) which estimates the yet-to-be-estimatedsymbols s_(n), s_(n+)1 . . . , s_(N). Because the interferencecancellation (decision feedback) likely is not perfect, further suppressthe interfering symbols by use of a modified linear equalizer filterF^((n)) which derives from the portion of the channel matrix fromsources (antennas) n, n+1, . . . , N. That is, z^((n))=F^((n))G^(H)[r−Gŝ^((n−1))] where the matrix F^((n)) ignores the portion of the channelrelating to the previously-estimated symbols (and analogously Grestricted to already estimated symbols and G^(H) restricted to ignorethese channels). The particular form of F^((n)) depends upon the lineardetector type and on assumption about the decision feedback error. Ineffect, the channel matrix is partitioned into two parts with the partrelating to the previously-estimated symbols used to generate theinterference estimate plus interference-cancelled signal and with thepart relating to the yet-to-be-estimated symbols used for detection ofthe interference-cancelled signal.

[0054] (d) Make a hard decision on the pth component of the softestimate z^((n)) to generate the hard estimate ŝ^(p) and update the hardestimate vector ŝ^((n)).

[0055] In particular, for assumed error-free decision feedback and IZFdetection; ${F^{(n)}G^{H}} = \begin{bmatrix}0_{{({n - 1})}{xQ}} \\{\left\lbrack {A_{n}^{H}A_{n}} \right\rbrack^{- 1}A_{n}^{H}}\end{bmatrix}$

[0056] where A_(k) is the NQ×(N−n+1) matrix of the last N−n+1 columns ofblocks of G; that is, A_(n)=[g_(n)g_(n+1) . . . g_(N)] with g_(k) thekth column (NQ×1) of the NQ×N channel matrix G. Of course, g_(k) is thechannel of the kth symbol from the weighted P antennas to the receivedNQ-vector. Then the SINR(n) is given by:SIN  R(n) = ρ/[A_(n)^(H)A_(n)]_(1, 1)⁻¹

[0057] And the antenna weightings follows as before from maximizing theminimum SINR(n).

[0058] Analogously for IMMSE in which ${F^{(n)}G^{H}} = \begin{bmatrix}0_{{({n - 1})}{xQ}} \\{\left\lbrack {{A_{n}^{H}A_{n}} + {\rho^{- 1}I_{N - n + 1}}} \right\rbrack^{- 1}A_{n}^{H}}\end{bmatrix}$

[0059] and the resulting SINR can be written asSIN  R(n) = ρ/[A_(n)^(H)A_(n) + ρ⁻¹I_(N = n + 1)]_(1, 1)⁻¹ − 1

[0060] Ordered iterative detection based on the symbol post-detectionSINR is often used to reduce the effect of decision feedback error. Letthe detection order be π(1), π(2), . . . , π(N) where π( ) is apermutation of the N integers {1,2, . . . ,N}; that is, the firstestimated symbol (hard estimate output of the first step of theiteration) will be ŝ_(π(1)) and the corresponding nonzero element ofŝ⁽¹⁾. The maximum SINR of the components of the first soft estimate z⁽¹⁾which estimates all P symbols, determines π(1). Similarly, the SINRs ofthe components of z⁽²⁾, which estimates all of the symbols excepts_(π(1)), determines π(2), and so forth. The partitioning of the channelmatrix at each step is analogous.

[0061] Note that the soft estimates z₁, z₂, . . . , z_(N) for thetransmitted block of symbols s₁, s₂, . . . , s_(N) (i.e., the outputz^((n)) _(π(n)) from the nth step) are used in a sequence decoder, suchas a Viterbi decoder or a Turbo decoder, in the form of log likelihoodratios (LLRs).

[0062] Other detection schemes are also possible. For example, areceiver consisting of a channel equalizer (to equalize H instead of G)followed by coherent combining with (I_(N)

w) can be used. In this case, the operation can be described as follows:

z=(I _(N)

w^(H))Fr

[0063] where F=[ρ⁻¹I_(NQ)+H^(H)H]⁻¹ (LMMSE equalizer) or F=[H^(H)H]⁻¹(LZF equalizer, which requires Q≧P). In this case, channel equalizationis performed to remove the effect of multipath (frequency selectivity).Then, coherent combining with the weight vector is performed insymbol-by-symbol basis. Closed-form expressions of SINR can also bederived from the definition. In practice, such channel equalizer can beimplemented as an adaptive filter. Note that this scheme is inferior tothe previous equalization scheme as this scheme does not exploit theknowledge of w in equalization. Utilizing w as in the previous schemeenables signal space (P-fold) dimensionality reduction.

[0064] 3. CDMA-Based Single Stream Preferred Embodiments

[0065] A CDMA system can have multiple mobile users for the samedownlink transmissions from a base station; the uplink channels fordifferent mobiles users are generally different, but for downlink theusers experiences a common channel. For the general case of K usersafter collecting samples of the received signal at the chip rate, thebaseband received signal NN_(C)Q-vector (where N_(C) is the CDMAspreading factor) can be written as:$r = {{{\sum\limits_{1 \leq \quad k \leq \quad K}{\left. \sqrt{}P_{k} \right.{H_{k}\left( {C_{k} \otimes I_{P}} \right)}\left( {I_{N} \otimes w_{k}} \right)s_{k}}} + {noise}} = \quad \left\lbrack {{\left. \sqrt{}P_{1} \right.{H_{1}\left( {C_{1} \otimes I_{P}} \right)}\left( {I_{N} \otimes w_{1}} \right)},{\left. \sqrt{}P_{2} \right.{H_{2}\left( {C_{2} \otimes I_{P}} \right)}\left( {I_{N} \otimes w_{2}} \right)}\quad,\quad {{\left. {\quad{\ldots \quad,{\left. \sqrt{}P_{K} \right.{H_{K}\left( {C_{K} \otimes I_{P}} \right)}\left( {I_{N} \otimes w_{K}} \right)}}} \right\rbrack \begin{bmatrix}s_{1} \\s_{2} \\\vdots \\s_{K}\end{bmatrix}} + {noise}}} \right.}$

[0066] where K is the number of users, P_(k) is the power of the kthuser, N is the symbol block size, H_(k) is the NN_(C)Q×NN_(C)P channelmatrix of the kth user, C_(k) is the NN_(C)×N CDMA spreading code matrixof the kth user, w_(k) is the weight vector of user k, and s_(k) is ablock of symbols of user k. In this case multiuser interferencecancellation (also known as multiuser detection) is needed. Similar toequalization, linear or iterative interference cancellation (ZF or MMSE)can be used and the SINR can be computed in the same manner as for thetime-division case by considering the total multiuser channel matrix$H_{tot} = \left\lbrack {{\left. \sqrt{}P_{1} \right.{H_{1}\left( {C_{1} \otimes I_{P}} \right)}\left( {I_{N} \otimes w_{1}} \right)},{\left. \sqrt{}P_{2} \right.{H_{2}\left( {C_{2} \otimes I_{P}} \right)}\left( {I_{N} \otimes w_{2}} \right)},\ldots \quad,{\left. \sqrt{}P_{K} \right.{H_{K}\left( {C_{K} \otimes I_{P}} \right)}\left( {I_{N} \otimes w_{K}} \right)}} \right\rbrack$

[0067] so H_(tot) is an NN_(C)Q×NK matrix for a Q-antenna receiver. Forexample, the linear ZF and MMSE multiuser interference cancellation forCDMA are $\begin{matrix}{z = {\left\lbrack {z_{1}^{T}z_{2}^{T}\quad \ldots \quad z_{K}^{T}} \right\rbrack^{T} = {Fr}}} \\{{F = {\left\lbrack {H_{tot}^{H}H_{tot}} \right\rbrack^{- 1}{H_{tot}^{H}({LZF})}}},{and}} \\{F = {\left( \left. \left\{ {{H_{tot}^{H}H_{tot}} + {\rho^{- 1}I_{NKP}}} \right. \right\rbrack \right)^{- 1}{H_{tot}^{H}({LMMSE})}}}\end{matrix}$

[0068] The SINR for each symbol from each CDMA user can also be definedin the same manner as that for TDMA. Similarly, iterative detectors forCDMA are analogous to that for CDMA. In practice, linear multiuserinterference cancellation can be implemented in successive or parallelarchitecture.

[0069] For downlink applications where the H_(k) are all the same(H_(k)=H) but w_(k) is user-specific (multiple users) interferencecancellation described above is a good alternative. Another possiblereceiver scheme for user k consists of a channel equalizer (whichlinearly equalizes only the channel H), the kth user despreader(multiplication with C_(k) ^(H)

I_(P)), and symbol-by-symbol coherent combining with the weight vector(multiplication with (I_(N)

w_(k) ^(H))). Again, the SINR expression for each symbol from user k canbe derived from SINR definition. In this downlink scenario, theweighting vectors for all of the users can be jointly selected at thebase station maximizing the minimum SINR across all users and symbols(similar to the previous preferred embodiments for equalizers). Thisensures that all of the users experience good performance.

[0070] For the downlink applications where both the H_(k) and the w_(k)are common (one user with multiple codes: H_(k)=H, w_(k)=w), the aboveinterference cancellation and equalization techniques are applicable. Inthis single-user multi-code downlink scenario, another receiver schemecan be derived by using the following identity:H(C_(k) ⊗ I_(P))(I_(N) ⊗ w) = H(I_(NNc) ⊗ w)C_(k) = H_(eff)C_(k)

[0071] The new receiver consists of an equalizer for the effectivechannel H_(eff)=H(I_(NNc)

w) followed by a despreader for user k (multiplication with C_(k) ^(H)).For weight vector selection, only one weighting vector needs to bedetermined, and maximizing the minimum SINR criteria again is used.

[0072] For TDMA- and CDMA-based systems, other types of equalizersand/or interference cancellers can be designed for mitigating the effectof multipath interference when closed-loop transmit diversity is used.For each type of multipath interference-resistant receiver, anexpression of SINR as a function of the channel realization, spreadingcode matrices (for CDMA), and weight vectors can be derived and used forpreferred embodiment weight vector selection.

[0073] 4. Multiplexed Streams Preferred Embodiments

[0074] The multiplexed stream preferred embodiments combine transmitdiversity and multiple data streams to achieve higher data rates. FIGS.2b-2 c illustrate transmitters and FIG. 3b shows a receiver. One datastream coming from the the symbol mapper is split into M streams. Aswith the foregoing preferred embodiments, the multiplexed streampreferred embodiment methods determine the antenna weightings fromcomposite channel characteristics.

[0075] In more detail, FIG. 2b illustrates a generic preferredembodiment transmitter with M=P/2 units with each unit having twoantennas and transmitting one data stream. Each w_(m) is a 2×1 weightingvector corresponding to the mth data stream; and the preferredembodiment methods provide composite channel determination of thew_(m)s.

[0076] More generally, each unit could have K antennas and thus M=P/K.The number of transmit antennas P must be a multiple of K. Of course,this scheme can be further generalized by accommodating the possibilityof each unit having different number of antennas. That is, group n isassigned to K_(n) antennas, where K₁+K₂+ . . . +K_(M)=P, where1≦K_(m)≦P, M≧2, and P>2. For simplicity, we assume that all the unitshave the same number of antennas for the rest of this description(extension to the most general case is obvious for one skilled in theart).

[0077]FIG. 2c illustrates preferred embodiments with a P×M lineartransformation (weighting matrix) of the M data streams onto the Pantennas.

[0078] Thus the FIG. 2b transmitter is a special case with weightingmatrix V given by: $V = \begin{bmatrix}w_{1} & 0 & \ldots & 0 \\0 & w_{2} & \ldots & 0 \\\vdots & \vdots & ⋰ & \vdots \\0 & 0 & \ldots & w_{M}\end{bmatrix}$

[0079] For systems with P transmit antennas the peak data rate is thesum of peak data rates of all of the streams. When all of the datastreams share the same modulation-coding scheme, the peak data rate issimply M times the peak data rate dictated by the modulation-codingscheme.

[0080] Consider the P-antenna transmitter and Q-antenna receiver systemof FIGS. 2c,3b. Denote the data (symbol) streams s₁, s₂, . . . , s_(M)by the M-vector s, and denote the Q×P channel by H. For simplicity,assume that the channel is frequency non-selective (single-tap),although extensions to multipath scenarios follow as analogs of thesingle-stream preferred embodiment systems described above. Then thereceived Q-vector signal can be written as:

r=HVs+noise

[0081] where the noise is Q-vector AWGN. The Q×M matrix HV is theeffective MIMO channel matrix, which includes spatial interference amongthe symbol streams. To generate sufficient statistics for detection,perform maximal ratio combining (MRC) matched filtering as with thesingle-stream preferred embodiments: $\begin{matrix}{y = {V^{H}H^{H}r}} \\{= {V^{H}{H^{H}\left( {{HVs} + {noise}} \right)}}}\end{matrix}$

[0082] Again, various detection methods may be applied; namely, maximumlikelihood, zero-forcing (both linear and iterative), and minimum meansquare error (both linear and iterative). The maximum likelihooddetection solves the following optimization problem:

ŝ=arg min _(s) ∥r−H V s ∥ ²

[0083] where the minimization is taken over the set of possibletransmitted symbol vectors which depends on the symbol mapping. Thelinear detection methods apply a linear transformation F to the receivedy to yield the soft estimation statistic z=F y by choice of F for ZF andMMSE as:

F _(LZF) =[V ^(H) H ^(H) HV] ⁻¹

F _(LMMSE) =[V ^(H) H ^(H) HV+I _(M)/ρ]⁻¹

[0084] where ρ is the average symbol power in the sense thatE[ss^(H)]=ρI_(M).

[0085] Iterative detectors are constructed from a series of lineartransformations followed by decision-feedback interference cancellation;as described in previous preferred embodiments. Again, the cancellationcan be ordered according to criteria such as largest SINR.

[0086] The multipath channel aspect is treated as in the previouspreferred embodiments. In this case, the SINR metric must incorporatethe effect of multipath interference.

[0087] The preferred embodiment weighting vectors/matrix determinationagain minimizes the symbol error rate (SER) for maximum likelihooddetection, and maximizes the minimum SINR for linear and iterativedetections; although other criteria could be used. And the resultantweighting vectors/matrix found by the receiver can be signaled to thetransmitter in the feedback channel for an FDD system but may,optionally, be directly determined by the transmitter for a TDD system.Analogous to the single-stream embodiment, the following SER upperboundcan be used:${SER} \leq \quad {\sum\limits_{z \in \Delta}{\kappa_{z}{Q\left( \sqrt{{{HVz}}^{2}/2} \right)}}}$

[0088] where Δ={(u−v): u,v εS, u≠v }, S is the set of all possibletransmitted symbol vectors, κ_(z) is the multiplicity of z in S, andQ(.) is the Gaussian Q-function. This upper bound can be used forselecting V: choose V that minimizes the SER upper bound. The preferredembodiments for linear and iterative detectors find the weights V bymaximization:

V=arg max _(UεS) min _(1≦m≦M) SINR(m; H, U)

[0089] where SINR(m; H, U) is the signal-to-interference+noise ratio forthe mth stream with channel H and weighting matrix U in the set S ofallowable weighting matrices. This criterion corresponds to minimizingthe system bit error rate (BER).

[0090] Closed-form expression of SINR(m; H, U) for different detectorscan be obtained. Define the following: $\begin{matrix}\begin{matrix}{A = {HU}} & {\left( {a\quad {QxM}\quad {matrix}} \right)} \\{{= \left\lbrack {a_{1},a_{2},\quad \ldots \quad,a_{M}} \right\rbrack}\quad} & {\left( {{each}\quad a_{m\quad}{is}\quad a\quad {Qx1}\quad {vector}} \right)} \\{{A_{m} = \left\lbrack {a_{m},a_{m + 1},\quad \ldots \quad,a_{M}} \right\rbrack}\quad} & {\left( {a\quad {{Qx}\left( {M - m + 1} \right)}\quad {matrix}} \right)}\end{matrix} \\{Then} \\{{{SINR}_{LZF}\left( {{m;H},U} \right)} = {\rho/\left\lbrack {A^{H}A} \right\rbrack_{m,m}^{- 1}}} \\{{{SINR}\left( {{m;H},U} \right)}_{LMMSE} = {{\rho/\left\lbrack {{I_{M}/\rho} + {A^{H}A}} \right\rbrack_{m,m}^{- 1}} - 1}} \\{{{SINR}_{IZF}\left( {{m;H},U} \right)} = {\rho/\left\lbrack {A_{m}^{H}A_{m}} \right\rbrack_{1,1}^{- 1}}} \\{{{SINR}\left( {{m;H},U} \right)}_{IMMSE} = {\rho/\left\lbrack {{I_{M}/\rho} + \left\lbrack {A_{m}^{H}A_{m}} \right\rbrack_{1,1}^{- 1} - 1} \right.}}\end{matrix}$

[0091] And for ordered detection in the iterative detectors, the SINRexpressions are accordingly modified as previously described.

[0092] The foregoing gives the criterion to select the optimal weightingmatrix V from the pre-determined set of allowable weighting matrices,S_(V). Another aspect of preferred embodiment systems is the selectionof this set S_(V) of allowable weighting matrices. As given above, onepossibility is to choose the following parameterization of V:$V = \begin{bmatrix}w_{1} & 0 & \cdots & 0 \\0 & w_{2} & \cdots & 0 \\\vdots & \vdots & ⋰ & \vdots \\0 & 0 & \cdots & w_{m}\end{bmatrix}$

[0093] where each w_(m) belongs to a set of allowable (P/M)×1 vectorsS_(wm) as described above. There are several variations: (1) when allw_(m) are distinct, |S_(V)|=Π_(m)|S_(wm)|, so when all S_(wm)=S_(w),|S_(V)|=|S_(w)|^(M); (2) all w_(m) are equal to a single w, and thus|S_(V)|=|S_(w)|.

[0094] Note that V can be any P×M linear transformation, so anotherpossibility is $V = \begin{bmatrix}R_{1} \\R_{2} \\\vdots \\R_{P/M}\end{bmatrix}$

[0095] where R_(m) is a M×M unitary rotation matrix. In particular, forM=2: $R_{m} = \begin{bmatrix}{\cos \quad \theta_{m}} & {^{j\quad \varphi \quad m}\sin \quad \theta_{m}} \\{{- ^{{- j}\quad \varphi \quad m}}\sin \quad \theta_{m}} & {\cos \quad \theta_{m}}\end{bmatrix}$

[0096] where φ_(m) and θ_(m) can be quantized for low-complexitysearching to find V. See the following simulation section.

[0097] 5. Simulation Results

[0098] FIGS. 4-5 compare raw BER for the P=Q=4 cases of standard MIMO(64QAM single stream space diversity), a double STTD, and varioustwo-stream preferred embodiments (FIG. 2b) with iterative MMSE detection(with ordering) at 4-bps/Hz and 6-bps/Hz throughput. The curves are: (1)DSTTD: open loop with double STTD; (2) Mode1: FIG. 2d with weights w₁=½,w_(p)=exp(jφ_(p)) for p=2,3,4 where the φ_(p) are uniformly quantized to2 bits, so the allowable weight space size is 4³=64; (3) DTXAA . . . M1:weight matrix of two 2×1 phase vectors w₁ and w₂ with each phase of2-bit quantization, so a total weight space size of 16; (4) DTXAA . . .M3: weight matrix of two 2×1 vectors w₁ and w₂ with each vector of 1-bitmagnitude and 2-bit phase quantization, so a total weight space size of64; (5) DTXAA . . . Rot N=4: weight matrix (FIG. 2c) is 2×1 of 2×2blocks with each 2×2 block a rotation by θ_(m) with θ_(m) uniformlyquantized in range [0,π/2) to 4 values, so the weight space size is 16;and (6) DTXAA . . . Rot N=8: weight matrix is 2×1 of 2×2 blocks witheach 2×2 block a rotation by θ_(m) with θ_(m) uniformly quantized inrange [0,π/2) to 8 values, so the weight space size is 64. Observe thatfor the same set size, the DTXAA (preferred embodiments) outperforms theconventional by up to 2.5 dB. Even with smaller set size, the preferredembodiments still outperfoms the conventional by up to 2.2 dB. Note thatcurve (4) performs the best.

[0099] 6. Modifications

[0100] The preferred embodiments can be modified in various ways whileretaining the features of antenna weightings determined from thecomposite channel.

[0101] For example, as mentioned before, other receiver schemes can beused, which result in different error rate or SINR dependence upon thechannel and weight vectors. For TDMA and CDMA systems, the channel mayexhibit significant frequency selectivity due to multipath effect. Inthis case, the weight selection criterion must incorporate the effect ofmultipath interference as well as the receiver scheme that is used tosuppress multipath interference. Finally, this scheme can also beapplied in OFDM-type systems, where the scheme is applied for eachsub-carrier or across sub-carriers.

What is claimed is:
 1. A method of transmission, comprising: (a)providing P transmit antennas where P is an integer greater than orequal to 3; (b) providing estimates for communication channels from saidP antennas to Q receive antenna(s) where Q is a positive integer; (c)providing M symbol streams to transmit from said P antennas through saidcommunication channels where M is an integer greater than or equal to 2;(d) providing weights for said P antennas, said weights multiplying saidsymbols.
 2. The method of claim 1, wherein: (a) said providing weightsof step (d) of claim 1 includes maximizing a minimumsignal-to-interference-plus-noise ratio of said symbol streams afterdetection.
 3. The method of claim 2, wherein: (a) said detection ofclaim 2 is selected from the group consisting of linear zero-forcing,linear minimum mean square error, iterative zero-forcing, and iterativeminimum mean square error.
 4. The method of claim 2, wherein: (a) saiddetection of claim 2 is maximum likelihood detector with SER selectioncriterion.
 5. The method of claim 1, wherein: (a) said weights of step(d) of claim 1 correspond to a diagonal M×M block matrix of (P/M)×1blocks where each (P/M)×1 block corresponds to weights for a symbolstream.
 6. The method of claim 1, wherein: (a) said weights of step (d)of claim 1 correspond to a (P/M)×1 block matrix of M×M blocks where eachM×M block is an M×M unitary matrix.
 7. The method of claim 1, wherein:(a) said providing channel estimates of step (b) of claim 1 is byreception from said receiver in said channel.
 8. The method of claim 1,wherein: (a) said providing channel estimates of step (b) of claim 1 isby reception of channel information from said receiver.
 9. The method ofclaim 1, wherein: (a) said channel estimates of step (b) of claim 1 areupdated; and (b) said weights of step (d) of claim 1 are updated inresponse to said channel estimates update.
 10. The method of claim 1,wherein: (a) said integer Q is greater than or equal to said integer P.11. The method of claim 1, wherein: (a) said communication channelsinclude multipath interference; and (b) said detection accounts for saidmultipath interference. Maximum likelihood+SER selection criterion. 12.The method of claim 11, wherein: (a) said detection of step (d) of claim1 is selected from the group consisting of linear zero-forcing, linearminimum mean square error, iterative zero-forcing, and iterative minimummean square error.
 13. The method of claim 1, wherein: (a) saidcommunication channel is a wideband or CDMA channel with multiple users.14. The method of claim 1, wherein: (a) said communication channel is awideband or CDMA channel with a single user with multiple codes.
 15. Themethod of claim 1, wherein: (a) said communication channel is anarrowband or TDMA channel.
 16. The method of claim 1, wherein: (a) saidcommunication channel is an OFDM channel.
 17. A transmitter, comprising:(a) P transmit antennas where P is an integer which is a multiple of aninteger M where M is greater than 1; (b) M data stream inputs coupled tosaid P antennas by PM weights; said weights determined from both (i)information of communication channels from said P antennas to Q receiveantenna(s) where Q is a positive integer, and a detection method forsaid M data streams at said Q antenna(s).
 18. The transmitter of claim17, wherein: (a) said weights are all 0 except for M sets, each set ofsize P/M, and each of said M sets couples one of said data streams to aset of P/M antennas.
 19. The transmitter of claim 17, wherein: (a) saidweights are partitioned into P/M sets, each set forming an M×M unitarytransformation of symbols at said M data stream inputs, and each of saidP/M sets couples said M data streams inputs to a corresponding set of Mantennas.
 20. The transmitter of claim 17, wherein: (a) said weights aredetermined outside of said transmitter and received by said transmitterfrom said signals from said Q antenna(s).
 21. A receiver, comprising:(a) Q antenna(s) where Q is a positive integer; (b) a detector coupledto said Q antenna(s), said detector adapted to detect signals from Ptransmit antennas where P is an integer greater than or equal to 3 andwith weights applied to said P antennas.
 22. The receiver of claim 21,wherein: (a) said weights combine symbols from M data streams and aredetermined from both (i) information of communication channels from saidP antennas to said Q receive antenna(s) plus (ii) a detection method ofsaid detector.
 23. The receiver of claim 21, wherein: (a) said detectionmethod is maximum likelihood with SER selection criterion.
 24. Thereceiver of claim 21, wherein: (a) said detection method is selectedfrom the group consisting of linear zero-forcing, linear minimum meansquare error, iterative zero-forcing, and iterative minimum mean squareerror; and (b) said weights determination includes maximization ofminimum signal-to-interference-plus-noise ratio.